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Updated: Sep 8, 2025

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深度学习在初级农产品新鲜度评估中的应用:系统性审查
Yifan Kang1, Yijie Li1, Hanyu Wang1
1College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China.
Journal of food science
|September 7, 2025
概括
集成机器视觉,光谱和电子鼻子的深度学习为评估农产品新鲜度提供了快速而准确的解决方案. 这种智能方法克服了传统方法的局限性,确保了食品安全和质量.
科学领域:
- 农业科学
- 食品科学
- 人工智能
背景情况:
- 农业初级产品至关重要,但容易降低新鲜度和腐败.
- 目前的评估方法 (传感,光谱,色度测量) 是主观的,需要大量的技能和时间.
- 降解会影响感官质量,营养,并可能导致有害物质的积累.
研究的目的:
- 审查深度学习的应用,以评估农业初级产品的新鲜度.
- 探索各种传感技术与深度学习模型的整合.
- 确定智能新鲜度评估的局限性和未来方向.
主要方法:
- 机器视觉 (物理化学特性,智能视觉标签)
- 光谱 (超光谱成像,近红外,光,拉曼)
- 电子鼻子
- 深度学习算法用于特征提取和模式识别
主要成果:
- 深度学习可以实现自动化,智能化和快速的新鲜度评估.
- 多种传感技术的整合提高了准确性,减少了主观性.
- 训练有素的模型可以预测与产品新鲜度相关的复杂模式.
结论:
- 深度学习显著改善了初级农产品的新鲜度评估.
- 将深度学习与先进的传感技术相结合为食品质量和安全提供了有前途的未来.
- 需要进一步的研究来解决目前的技术局限性.
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